Abstract
Wearable devices have been used widely for human activity recognition in the field of pervasive computing. One big area of in this research is the recognition of activities of daily living where especially inertial and interaction sensors like RFID tags and scanners have been used. An issue that may arise when using interaction sensors is a lack of certainty. A positive signal from an interaction sensor is not necessarily caused by a performed activity e.g, when an object is only touched but no interaction occurred afterwards. In our work, we aim to overcome this limitation and present a multi-modal egocentric-based activity recognition approach which is able to recognize the critical activities by looking at movement and object information at the same time. We present our results of combining inertial and video features to recognize human activities on different types of scenarios where we achieve a $F_{1}$ -measure up to 79.6%.
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